Type 2 diabetes mellitus (T2D)-associated end-stage renal disease (ESRD), or T2D-ESRD, is responsible for 44.6% of all cases of chronic renal failure in the United States. The devastating health effects of T2D-ESRD are disproportionately borne by the African American community as represented by an incidence rate of 1010 cases per million, or more than three times that of Caucasians. It is widely known that there is a significant, albeit poorly understood, genetic component to this disparity as incidence for African Americans remains significantly greater than other ethnic groups when socioeconomic status is controlled for. A Genome-Wide Association Study investigating T2D-ESRD trait variants in African Americans identified a number of potentially significant hits but was able to explain only modest risk association. One likely source of this missing heritability is low frequency coding variants. Herein, this proposal seeks to identify low frequency and common coding variants that contribute to T2D-ESRD in African Americans through a multiphase study model that incorporates next-generation exome sequencing with powerful bioinformatics tools. Deep exome sequencing performed on 500 African American T2D-ESRD cases and 500 population based controls identified a substantial list of candidate T2D-ESRD variants in the first phase of the study. The second phase of the study will include replication of these coding variants in an additional cohort followed by a trait discrimination phase to determine their contribution to T2D, T2D-ESRD, or ESRD. The statistical analyses used to detect associations in the first two phases will incorporate multiple models designed to identify low frequency and common coding variants, and will also be comprehensive, adjusting for admixture as well as important covariates. Finally, bioinformatic tools including structural modeling, expression profiling, and pathway analyses will be performed in order to investigate the genetic architecture of T2D-ESRD in African Americans. This proposal incorporates state of the art technologies and statistical methods to shed light on a health disparity of national attention through an efficientl designed study paradigm.